mlsanityData to 14 Oct 2026

NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer

arXiv 2410.11924 ↗First posted 15 Oct 2024cs.LGcited by 4

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Abstract (arXiv)

Nuclear radiation, which refers to the energy emitted from atomic nuclei during decay, poses significant risks to human health and environmental safety. Recently, advancements in monitoring technology have facilitated the effective recording of nuclear radiation levels and related factors, such as weather conditions. The abundance of monitoring data enables the development of accurate and reliable nuclear radiation forecasting models, which play a crucial role in informing decision-making for individuals and governments. However, this task is challenging due to the imbalanced distribution of monitoring stations over a wide spatial range and the non-stationary radiation variation patterns. In this study, we introduce NRFormer, a novel framework tailored for the nationwide prediction of nuclear radiation variations. By integrating a non-stationary temporal attention module, an imbalance-aware spatial attention module, and a radiation propagation prompting module, NRFormer collectively captures complex spatio-temporal dynamics of nuclear radiation. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed framework against 11 baselines.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

32 of 58 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  4. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  8. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  9. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  10. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  11. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  12. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
20 more
  1. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 210
  2. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 151
  3. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
  4. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  5. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 117
  6. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 113
  7. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  8. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  9. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  10. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  11. 2020Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networkscited by 39
  12. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  13. 2022Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Modelscited by 29
  14. 2021MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisalcited by 20
  15. 2021Shifted Chunk Transformer for Spatio-Temporal Representational Learningcited by 20
  16. 2023On Structural Expressive Power of Graph Transformerscited by 19
  17. 2023LightCTS: A Lightweight Framework for Correlated Time Series Forecastingcited by 18
  18. 2024AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecastingcited by 10
  19. 2023Sparse Binary Transformers for Multivariate Time Series Modelingcited by 8
  20. 2023Machine Learning for Urban Air Quality Analytics: A Surveycited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecastingcited by 2
  2. 2026In Transformer We Trust? A Perspective on Transformer Architecture Failure Modescited by 1
  3. 2026Nested Spatio-Temporal Time Series Forecastingcited by 1
  4. 2026Atmospheric Diffusion-Guided Spatio-Temporal Transformer for Nuclear Radiation Forecastingcited by 0